With the analysis of SWIR and NIR spectral space, and based on the Short Wave Perpendicular Water Stress Index (SPSI) of
which was constructed by Abduwasit Ghulam, the SPSI was applied in the soil moisture retrieval during the wheat growing
period in April with full cover condition. The result showed there is a high correlation coefficient between SPSI and soil
moisture in depth of 0-30 cm, and has been tested that the SPSI is an effectiveness index in the soil moisture retrieval under
full cover condition.
In this paper, the analysis methods, which used to descript the winter wheat growing features, have been found of satellite
remote sensing coupled with the data of LAI, dry matter weight, etc. the results showed that the revised rate of remote
sensing to in-situ observation is different in different developmental stages of winter wheat. Mainly manifested in the
following aspects (1) In the early stage of growth and development of winter wheat (in March), the leaf area index LAI of
winter wheat is small, due to the impact of soil background, the winter wheat NDVI which retrieved from MODIS data (leaf
area index LAI can be calculated from NDVI) are vary greatly from in-situ observations, the revised coefficient is relatively
large. (2) In the rapid vegetative growth stage (April), the ground was completely covered by winter wheat, the influence of
soil background decreased, and LAI which retrieved from remote sensing closing to the data in-situ observation accordingly
and the revised coefficient is smaller than in the early stage of winter wheat. (3) The LAI decreased sharply in the later stage
of winter wheat. So the LAI accuracy of remote sensing retrieval as well as reduced. The differences are largest between the
remote sensing retrieval and in-situ observation, and the revised coefficient is largest in all growing stage.
To correct erroneous data arising from a variety of methods for monitoring soil drought, the paper presents the analysis of the
crop-canopy spectral characteristics and measured field moisture in the mid-late stage of wheat grain filling by means of observations
of synchronously monitoring drought at Satellite -airborne - in situ observation in an experiment made in the low land of the Yellow
River reach in a Zhoukou farm of the province on 23 May, 2009. Results suggest that (1) In the later time of wheat grain filling, there
was no clear absorption valley in the domain of 1175nm, and it is different from the spectral chart in the period of turning green to
heading. (2) There are data distortions in the domain of 1541nm and 2053nm which make out that the spectral in these domain are
disable for retrieved the wheat canopy character. (3) The relationship of one depth to adjacent is better and the soil moisture in deeper
depth could be deduced from its relationship with surface water content. (4) The retrieved results of FY-3A are not better than MODIS',
but the accuracy has been to meet the current demand for services, and can be applied to operation.
In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen
model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system
in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of
remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at
home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth
models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as
the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the
method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the
simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation
accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in
previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc.,
crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and
quantitative assessment techniques for crop growth in future.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectral interpretation of vegetation and soil are serious factors in the judgment of drought degree. Based on
the spectral character of water, recently, a new model of Surface Water Capacity Index (SWCI) has been put
forward, and the index is more sensitive to the surface water content, and suit for regional drought
monitoring. The comparative analysis showed: SWCI is more sensitive than NDVI to monitoring surface soil
water content; this is available in real-time soil drought monitoring.
The thesis, on the basis of the researches in the past, discusses the researches on agricultural drought
monitoring, forecasting and loss assessment evaluation as well as its application status in China. While
discussing and comparing different soil moisture monitoring methods, the thesis also introduces Gstar-1
which is an automatic soil moisture observer with independent property right, and CSMI which is the
new remote sensing monitoring index for soil moisture on the basis of MODIS data, and gives a
comprehensive introduction to the loss assessment of China. Through the real-time monitoring,
forecasting and assessment of drought occurrence and development, the thesis is dedicated to reducing
the influence of drought to agricultural production to the largest extent. At last, on the basis of the
problems in research, the thesis proposes the future research direction.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectrum interpretation of vegetation and soil are serious factors in the judgment of drought degree. To find a
more real-time monitoring index of cropland soil moisture by remote sensing, a Cropland Soil Moisture Index
(CSMI) was established in this paper based on the effective reflections of Normalized Difference Vegetation
Index (NDVI) on deeper soil moisture and well expressions of Surface Water Content Index (SWCI) on
surface soil moisture. By validation with different time-series MODIS data, the Cropland Soil Moisture Index
(CSMI) not only overcome the limitation of hysteretic nature and saturated quickly of Normalized Difference
Vegetation Index (NDVI), but also take the advantage of the Surface Water Content Index (SWCI) which
effectively reduce the atmosphere disturbance and retrieval surface soil water content better. The index passed
the significant F-tests with α = 0. 01, and is a true real-time drought monitoring index.
The method of Normalized Multi-Band Drought Index (NMDI) is constructed by fully considered the
channel 2 (860nm) sensitive to leaf water content changes and the difference between two liquid water
absorption bands (1640 nm and 2130 nm) as the soil and vegetation water sensitive band. The potential
have been confirmed with the application in different time-series MODIS data. The results show: there is a
significant correlation between Normalized Multi-Band Drought Index (NMDI) and soil moisture, the
index adopted passed the significant F-tests with α = 0. 01. So the method of Normalized Multi-Band
Drought Index (NMDI) could be used in Henan drought monitoring. We found that the index of NMDI
application to areas with moderate vegetation coverage, however, needs further investigation.
Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the
spectrum interpretation of vegetation and soil are serious factors in the judgment of drought degree. To find a
more real-time monitoring index of cropland soil moisture by remote sensing, a Cropland Soil Moisture Index
(CSMI) was established in this paper based on the effective reflections of Normalized Difference Vegetation
Index (NDVI) on deeper soil moisture and well expressions of Surface Water Content Index (SWCI) on
surface soil moisture. By validation with different time-series MODIS data, the Cropland Soil Moisture Index
(CSMI) not only overcome the limitation of hysteretic nature and saturated quickly of Normalized Difference
Vegetation Index (NDVI), but also take the advantage of the Surface Water Content Index (SWCI) which
effectively reduce the atmosphere disturbance and retrieval surface soil water content better. The index passed
the significant F-tests with α = 0. 01, and is a true real-time drought monitoring index.
Soil moisture and vegetation growth are the most important and direct index for drought. The interpreting to vegetation and
spectrum analysis of soil are two important elements in the judgment of drought. Recently, Abduwasit Ghulam and other
researchers, on the basis of the spatial distribution characters of soil moisture in near infrared spectrum, adopt the expansion
analysis method and establish PDI. Later, vegetation index is introduced by establishing a new drought monitoring method MPDI
after comprehensively considering about the soil moisture and vegetation growth characters. The article, directing against the
drought in different periods of Henan Province, adopts the MODIS image data to undertake PDI and MPDI calculations and
compares with the soil moisture data with that of the same period, concluding that: PDI and MPDI are closely related with the
original data from land observation, among which the relations between MPDI and 0-20cm calculation is the closest; PDI and
MPDI are all close to the drought situation concluded from the calculation of bared land and the early growth period of
vegetations; MPDI is more suitable for the areas with vegetations.
Soil moisture and vegetation growth are the most important and direct index for drought. The interpreting to vegetation and
spectrum analysis of soil are two important elements in the judgment of drought. Recently, Abduwasit Ghulam and other
researchers, on the basis of the spatial distribution characters of soil moisture in near infrared spectrum, adopt the expansion
analysis method and establish PDI. Later, vegetation index is introduced by establishing a new drought monitoring method MPDI
after comprehensively considering about the soil moisture and vegetation growth characters. The article, directing against the
drought in different periods of Henan Province, adopts the MODIS image data to undertake PDI and MPDI calculations and
compares with the soil moisture data with that of the same period, concluding that: PDI and MPDI are closely related with the
original data from land observation, among which the relations between MPDI and 0-20cm calculation is the closest; PDI and
MPDI are all close to the drought situation concluded from the calculation of bared land and the early growth period of
vegetations; MPDI is more suitable for the areas with vegetations.
The diversity of the spatial scale of landscape raises the requirement of multiscale analysis of remote sensing (RS) images. Usually the first step to analyze remote sensing images is image segmentation, in which the muitiscale effect should be taken into account to achieve satisfactory segmentation results. This paper describes an effective approach to segment remote sensing images in multiscale. Based on the fact that in a specific scale of a remote sensing image the same objects are similar, the image is first segmented in a small scale by uniting the most similar objects. After that, a set of multiscale objects with full topological relationship can be obtained. Based on the set of multiscale objects, the authors explore the application of this approach in object-oriented information extraction from remote sensing images.
In this paper, a automatic approach of geometry precision checking for digital ortho-image (DOM), especially for a batch of DOM, is proposed. Automatic image matching between DOM with various resolution was performed in the DOM checking based on reference DOM with higher resolution. Automatic extraction of linear feature was used in the DOM checking based on reference vector map with higher precision. A lot of experiments testify that the method of this paper
can improve the efficency and reliability of checking method of DOM image for geometry precision.
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